3,276 research outputs found
Exploiting real-time 3d visualisation to enthuse students: A case study of using visual python in engineering
No full textWe describe our experience teaching programming and numerical methods to engineering students using Visual Python to exploit three dimensional real time visualisation. We describe the structure and content of this teaching module and evaluate the module after its delivery. We find that the students enjoy being able to visualise physical processes (even if these have effectively only 1 or 2 spatial degrees of freedom) and this improves the learning experience
ctypes. ctypes run!
Get PDFOne of the new features of Python 2.5 is the introduction of ctypes as a standard library module. At the simplest level, ctypes adds the standard C types to Python: signed and unsigned bytes, shorts, ints and longs; as well as structs, unions, pointers and functions. At run-time it can load a shared library (DLL) and import its symbols, allowing a Python application to make function calls into the library without any special preparation. ctypes can be used to wrap native libraries in place of interface generators such as SWIG, to manipulate memory and Python objects at the lowest level, and to prototype application development in other languages.This paper begins with a quick introduction to ctypes, shows some advanced techniques, and describes some examples of how it has been used by the author in his recent work
Axelrod-Python/Axelrod: v4.9.0
No full textv4.9.0, 2020-04-07
New strategies, new classifier system and internal improvements/fixes.
Cleanup the tests:
https://github.com/Axelrod-Python/Axelrod/pull/1308
Create function to handle internal file paths:
https://github.com/Axelrod-Python/Axelrod/pull/1307
Fix bug in Result set:
https://github.com/Axelrod-Python/Axelrod/pull/1305
Improve and expand LR Player's docstring
https://github.com/Axelrod-Python/Axelrod/pull/1303
New strategy classifier mechanism:
https://github.com/Axelrod-Python/Axelrod/pull/1300
Add new Gradual strategy:
https://github.com/Axelrod-Python/Axelrod/pull/1299
Add missing author to docs bibliography:
https://github.com/Axelrod-Python/Axelrod/pull/1295
Suppress numpy warnings:
https://github.com/Axelrod-Python/Axelrod/pull/1292
Fix documentation:
https://github.com/Axelrod-Python/Axelrod/pull/1291
Fix FirstByDowning:
https://github.com/Axelrod-Python/Axelrod/pull/1285
Add citations:
https://github.com/Axelrod-Python/Axelrod/pull/1281
https://github.com/Axelrod-Python/Axelrod/compare/v4.9.0...v4.8.
coin-or/python-mip: 1.14.1
No full textWhat's Changed
Introduce pyproject.toml by @sebheger in https://github.com/coin-or/python-mip/pull/283
Fix for TypeError: can only concatenate str (not "type") to str by @Markus28 in https://github.com/coin-or/python-mip/pull/288
Fix Gurobi objective by @Markus28 in https://github.com/coin-or/python-mip/pull/290
Fix removal of variables (#294) by @sebheger in https://github.com/coin-or/python-mip/pull/296
Update version of numpy by @sebheger in https://github.com/coin-or/python-mip/pull/301
New Contributors
@Markus28 made their first contribution in https://github.com/coin-or/python-mip/pull/288
Full Changelog: https://github.com/coin-or/python-mip/compare/1.14.0...1.14.
Evaluation of MIDI data processing function by Python + Mido library
Get PDFThis is a research note investigating the MIDI data processing function of the Mido library on Python. MIDI (Musical Instrument Digital Interface) is a standard for transmitting and receiving music performance information between electronic musical instruments and computers. Mido is a library that allows MIDI data to be handled as an object on the programming language Python. By using a library such as Mido, it can be expected to process the control and performance expression of various electronic musical instruments efficiently and flexibly. The author has made various attempts, such as converting information from various sensors into MIDI data based on certain rules, using a board computer such as Arduino or a personal computer. As a result of investigating the MIDI processing function of Mido this time, it was found to be sufficiently useful for the author's research. In the future, I would like to further explore the Python and Mido libraries and deepen my research activities.本稿はPython上のMidoライブラリによる、MIDIデータ処理機能について調べた研究ノートである。MIDI（Musical Instrument Digital Interface）とは，電子楽器やコンピュータ間で音楽の演奏情報を送受信するための規格である。またMidoは，MIDIデータをプログラム言語Python上のオブジェクトとして扱うことができるようにするためのライブラリで，Midoのようなライブラリを利用することで，様々な電子楽器の制御や演奏表現を効率よく，かつ柔軟に処理することが期待できる。著者はこれまでArduinoなどのボードコンピュータやパソコンを使い，各種センサーからの情報を一定ルールに基づきMIDIデータに変換するなど、様々な試みを行ってきたが，今回MidoによるMIDI処理機能を調べた結果、著者の研究に十分役立つことが分かった。今後はPythonやMidoライブラリをさらに探求し、より研究活動を深めて行きたい。departmental bulletin pape
coin-or/python-mip: Release 1.14.1
No full textWhat's Changed
Introduce pyproject.toml by @sebheger in https://github.com/coin-or/python-mip/pull/283
Fix for TypeError: can only concatenate str (not "type") to str by @Markus28 in https://github.com/coin-or/python-mip/pull/288
Pin pypy version for CI by @sebheger in https://github.com/coin-or/python-mip/pull/295
Update macos version in CI by @sebheger in https://github.com/coin-or/python-mip/pull/298
Added pre-commit by @Markus28 in https://github.com/coin-or/python-mip/pull/293
Fix Gurobi objective by @Markus28 in https://github.com/coin-or/python-mip/pull/290
Fix removal of variables (#294) by @sebheger in https://github.com/coin-or/python-mip/pull/296
Update version of numpy by @sebheger in https://github.com/coin-or/python-mip/pull/301
Remove and untrack mip/_version.py to make setuptools_scm work correctly by @sebheger in https://github.com/coin-or/python-mip/pull/302
New Contributors
@Markus28 made their first contribution in https://github.com/coin-or/python-mip/pull/288
Full Changelog: https://github.com/coin-or/python-mip/compare/1.14.0...1.14.
An Accessible Python based Author Identification Process
No full textAn author identification process using Python tools and using the Federalist Papers as a case study
Netherlands eScience Center Python Template
No full textSpend less time setting up and configuring your new Python packages and comply with the
Netherlands eScience Center Software Development Guide
from the start.
Added
Instructions to add your existing code to directory generated by the NLeSC Python template #202
Keywords to questionaire #270
Next step issue generation workflow #228
Next step issue for SonarCloud integration #234
Next step issue for Zenodo integration #235
Next step issue for Read the Docs #236
Next step issue for citation data #237
Next step issue for linting #238
Next steps documentation #240
Support for sub packages in distro #160
Tests for api doc generation #213
CI Tests on Windows #140 #223
.pylintrc file
Valid license name and first author name in CITATION.cff
SonarCloud integration for code quality and coverage #89
Read the Docs #78
Changed
Always generate API docs #176
Have 100% test coverage in generated code #88
Removed
Automatic publish to PyPi after GitHub release #196
</ul
NFDMLab: Simulating nonlinear frequency division multiplexing in Python
No full textFiber-optic transmission based on nonlinear frequency division multiplexing (NFDM) has received much attention in recent years. We introduce NFDMLab, an open source software package for simulating NFDM transmissions written in the Python language.Accepted Author ManuscriptTeam Sander Wahl
ManyTypes4Py: A benchmark python dataset for machine learning-based type inference
No full textIn this paper, we present ManyTypes4Py, a large Python dataset for machine learning (ML)-based type inference. The dataset contains a total of 5, 382 Python projects with more than 869K type annotations. Duplicate source code files were removed to eliminate the negative effect of the duplication bias. To facilitate training and evaluation of ML models, the dataset was split into training, validation and test sets by files. To extract type information from abstract syntax trees (ASTs), a light-weight static analyzer pipeline is developed and accompanied with the dataset. Using this pipeline, the collected Python projects were analyzed and the results of the AST analysis were stored in JSON-formatted files. The ManyTypes4Py dataset is shared on zenodo and its tools are publicly available on GitHub. Accepted author manuscriptSoftware Engineerin
- …
